Hybrid Modelling for the Failure Analysis of SiC Power Transistors on Time-Domain Reflectometry Data

• Published in: 2021 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) pp. 1 - 6
• Main Authors: Kamm, Simon, Sharma, Kanuj, Kallfass, Ingmar, Jazdi, Nasser, Weyrich, Michael
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.09.2021
• Subjects: Analytical models; Convolutional Neural Networks; Data models; Failure analysis; Hybrid Modelling; Long Short-Term Memory Networks; Machine Learning; Mathematical models; Reflectometry; Silicon carbide; Time-Domain Reflectometry; Transistors
• ISSN: 1946-1550
• DOI: 10.1109/IPFA53173.2021.9617401

Ensuring and improving the reliability of electronic devices requires post-production failure analysis processes. One of the techniques to perform failure analysis for electronic devices is Time-Domain Reflectometry. With this method, failures can be detected, located, and characterized non-destructively. It enables not only the detection of hard interconnection failures (such as an open or a short) but also the detection and characterization of soft failures. For Time-Domain Reflectometry, known physical equations can be applied to model the ideal behavior of a signal for different failures. However, since electronic device architectures and thus the data become more and more complex and measurements are disturbed by noise or nonoptimal material properties, these models solely are not sufficient for failure analysis. Therefore we propose hybrid modeling, where machine learning models (e.g. convolutional neural networks) work together with physical models to detect, locate, classify and characterize the failure. The first approach is shown on simulated microstrip line data and then transferred to SiC transistors for evaluation.